Genetic algorithms (GA), such as NSGA-II [1]}, are well known for dealing with mixed-variable spaces and finding an optimal Pareto-frontier. However, these algorithms require a large number of black-box function calls and are not well-suited to expensive small-data problems. Our approach is to use a GA to optimize the surrogate model itself and find a Pareto-optimal. Diversification is ensured by the distance metrics used while optimizing the surrogate model. This method allows cheap `Q-batch' samples from within the GA generation, and also allows the use of the commonly used acquisition functions such as Expected Improvement (EI), Probability of Improvement (PI) and Upper Confidence Bound (UCB) [2]}, which work well for single objective problems. We note here that hyper-volume improvement can easily be incorporated instead of a distance metric within the GA setting in future work. We also present a new acquisition function, `Stochastic Monte-Carlo' (SMC), which preforms well for categorical variable problems [3]}.
